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---
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library_name: transformers
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license: apache-2.0
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base_model: google/vit-base-patch16-224
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tags:
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- image-classification
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: vit-base-oxford-iiit-pets
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# vit-base-oxford-iiit-pets
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1798
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- Accuracy: 0.9310
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0003
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- train_batch_size: 512
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 100
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 1.0 | 12 | 2.6101 | 0.5223 |
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| No log | 2.0 | 24 | 1.7190 | 0.8227 |
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| No log | 3.0 | 36 | 1.0833 | 0.8890 |
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| No log | 4.0 | 48 | 0.7011 | 0.9120 |
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| No log | 5.0 | 60 | 0.5052 | 0.9242 |
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| No log | 6.0 | 72 | 0.4097 | 0.9310 |
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| No log | 7.0 | 84 | 0.3560 | 0.9350 |
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| No log | 8.0 | 96 | 0.3237 | 0.9337 |
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| 1.1364 | 9.0 | 108 | 0.3008 | 0.9378 |
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| 1.1364 | 10.0 | 120 | 0.2833 | 0.9364 |
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| 1.1364 | 11.0 | 132 | 0.2694 | 0.9391 |
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| 1.1364 | 12.0 | 144 | 0.2586 | 0.9391 |
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| 1.1364 | 13.0 | 156 | 0.2498 | 0.9418 |
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| 1.1364 | 14.0 | 168 | 0.2423 | 0.9405 |
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| 1.1364 | 15.0 | 180 | 0.2359 | 0.9405 |
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| 1.1364 | 16.0 | 192 | 0.2303 | 0.9459 |
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| 0.2326 | 17.0 | 204 | 0.2259 | 0.9405 |
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| 0.2326 | 18.0 | 216 | 0.2222 | 0.9405 |
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| 0.2326 | 19.0 | 228 | 0.2178 | 0.9432 |
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| 0.2326 | 20.0 | 240 | 0.2146 | 0.9445 |
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| 0.2326 | 21.0 | 252 | 0.2114 | 0.9432 |
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| 0.2326 | 22.0 | 264 | 0.2087 | 0.9445 |
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| 0.2326 | 23.0 | 276 | 0.2061 | 0.9432 |
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| 0.2326 | 24.0 | 288 | 0.2040 | 0.9459 |
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| 0.1651 | 25.0 | 300 | 0.2018 | 0.9459 |
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| 0.1651 | 26.0 | 312 | 0.2000 | 0.9445 |
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| 0.1651 | 27.0 | 324 | 0.1985 | 0.9459 |
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| 0.1651 | 28.0 | 336 | 0.1968 | 0.9472 |
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| 0.1651 | 29.0 | 348 | 0.1948 | 0.9459 |
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| 0.1651 | 30.0 | 360 | 0.1939 | 0.9459 |
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| 0.1651 | 31.0 | 372 | 0.1924 | 0.9459 |
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| 0.1651 | 32.0 | 384 | 0.1915 | 0.9459 |
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| 0.1651 | 33.0 | 396 | 0.1909 | 0.9459 |
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| 0.134 | 34.0 | 408 | 0.1894 | 0.9472 |
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| 0.134 | 35.0 | 420 | 0.1883 | 0.9459 |
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| 0.134 | 36.0 | 432 | 0.1877 | 0.9472 |
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| 0.134 | 37.0 | 444 | 0.1866 | 0.9486 |
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| 0.134 | 38.0 | 456 | 0.1863 | 0.9472 |
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| 0.134 | 39.0 | 468 | 0.1851 | 0.9486 |
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| 0.134 | 40.0 | 480 | 0.1843 | 0.9472 |
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| 0.134 | 41.0 | 492 | 0.1837 | 0.9472 |
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| 0.1128 | 42.0 | 504 | 0.1831 | 0.9459 |
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| 0.1128 | 43.0 | 516 | 0.1828 | 0.9472 |
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| 0.1128 | 44.0 | 528 | 0.1822 | 0.9472 |
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| 0.1128 | 45.0 | 540 | 0.1816 | 0.9472 |
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| 0.1128 | 46.0 | 552 | 0.1808 | 0.9459 |
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| 0.1128 | 47.0 | 564 | 0.1804 | 0.9459 |
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| 0.1128 | 48.0 | 576 | 0.1802 | 0.9459 |
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| 0.1128 | 49.0 | 588 | 0.1796 | 0.9459 |
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| 0.0999 | 50.0 | 600 | 0.1793 | 0.9472 |
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| 0.0999 | 51.0 | 612 | 0.1792 | 0.9486 |
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| 0.0999 | 52.0 | 624 | 0.1787 | 0.9472 |
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| 0.0999 | 53.0 | 636 | 0.1784 | 0.9472 |
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| 0.0999 | 54.0 | 648 | 0.1780 | 0.9459 |
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| 0.0999 | 55.0 | 660 | 0.1778 | 0.9445 |
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| 0.0999 | 56.0 | 672 | 0.1772 | 0.9445 |
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| 0.0999 | 57.0 | 684 | 0.1769 | 0.9472 |
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| 0.0999 | 58.0 | 696 | 0.1768 | 0.9472 |
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| 0.0894 | 59.0 | 708 | 0.1766 | 0.9472 |
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| 0.0894 | 60.0 | 720 | 0.1763 | 0.9472 |
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| 0.0894 | 61.0 | 732 | 0.1762 | 0.9486 |
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| 0.0894 | 62.0 | 744 | 0.1760 | 0.9472 |
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| 0.0894 | 63.0 | 756 | 0.1755 | 0.9459 |
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| 0.0894 | 64.0 | 768 | 0.1752 | 0.9459 |
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| 0.0894 | 65.0 | 780 | 0.1749 | 0.9459 |
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| 0.0894 | 66.0 | 792 | 0.1749 | 0.9459 |
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| 0.0828 | 67.0 | 804 | 0.1746 | 0.9472 |
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| 0.0828 | 68.0 | 816 | 0.1745 | 0.9459 |
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| 0.0828 | 69.0 | 828 | 0.1745 | 0.9459 |
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| 0.0828 | 70.0 | 840 | 0.1744 | 0.9459 |
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| 0.0828 | 71.0 | 852 | 0.1740 | 0.9459 |
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| 0.0828 | 72.0 | 864 | 0.1741 | 0.9459 |
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| 0.0828 | 73.0 | 876 | 0.1737 | 0.9459 |
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| 0.0828 | 74.0 | 888 | 0.1739 | 0.9459 |
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| 0.0778 | 75.0 | 900 | 0.1739 | 0.9459 |
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| 0.0778 | 76.0 | 912 | 0.1737 | 0.9459 |
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| 0.0778 | 77.0 | 924 | 0.1735 | 0.9459 |
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| 0.0778 | 78.0 | 936 | 0.1733 | 0.9459 |
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| 0.0778 | 79.0 | 948 | 0.1732 | 0.9459 |
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| 0.0778 | 80.0 | 960 | 0.1732 | 0.9459 |
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| 0.0778 | 81.0 | 972 | 0.1730 | 0.9459 |
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| 0.0778 | 82.0 | 984 | 0.1730 | 0.9459 |
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| 0.0778 | 83.0 | 996 | 0.1730 | 0.9459 |
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| 0.0738 | 84.0 | 1008 | 0.1729 | 0.9459 |
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| 0.0738 | 85.0 | 1020 | 0.1727 | 0.9459 |
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| 0.0738 | 86.0 | 1032 | 0.1726 | 0.9459 |
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| 0.0738 | 87.0 | 1044 | 0.1726 | 0.9459 |
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| 0.0738 | 88.0 | 1056 | 0.1726 | 0.9459 |
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| 0.0738 | 89.0 | 1068 | 0.1726 | 0.9459 |
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| 0.0738 | 90.0 | 1080 | 0.1725 | 0.9459 |
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| 0.0738 | 91.0 | 1092 | 0.1724 | 0.9459 |
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| 0.0715 | 92.0 | 1104 | 0.1724 | 0.9459 |
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| 0.0715 | 93.0 | 1116 | 0.1723 | 0.9459 |
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| 0.0715 | 94.0 | 1128 | 0.1723 | 0.9459 |
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| 0.0715 | 95.0 | 1140 | 0.1723 | 0.9459 |
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| 0.0715 | 96.0 | 1152 | 0.1722 | 0.9459 |
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| 0.0715 | 97.0 | 1164 | 0.1722 | 0.9459 |
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| 0.0715 | 98.0 | 1176 | 0.1722 | 0.9459 |
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| 0.0715 | 99.0 | 1188 | 0.1722 | 0.9459 |
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| 0.0701 | 100.0 | 1200 | 0.1722 | 0.9459 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1
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- Datasets 3.0.0
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- Tokenizers 0.19.1
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